library(tidyverse)
## ── Attaching packages ────────────────────────────────────────── tidyverse 1.3.0 ──
## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.2 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0
## ── Conflicts ───────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(gridExtra)
##
## Attaching package: 'gridExtra'
## The following object is masked from 'package:dplyr':
##
## combine
surveys_complete <- read_csv("data/surveys_complete.csv")
## Parsed with column specification:
## cols(
## record_id = col_double(),
## month = col_double(),
## day = col_double(),
## year = col_double(),
## plot_id = col_double(),
## species_id = col_character(),
## sex = col_character(),
## hindfoot_length = col_double(),
## weight = col_double(),
## genus = col_character(),
## species = col_character(),
## taxa = col_character(),
## plot_type = col_character()
## )
ggplot(data = surveys_complete, mapping = aes(x = weight, y = hindfoot_length)) +
geom_point()
surveys_plot <- ggplot(data = surveys_complete,
mapping = aes(x = weight, y = hindfoot_length))
surveys_plot +
geom_point()
ggplot(data = surveys_complete, mapping = aes(x = weight, y = hindfoot_length)) +
geom_point(alpha = 0.1, aes(color = species_id))
ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
geom_point(aes(color = plot_type))
# This is not the best way to show the data.
ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
geom_boxplot()
ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
geom_jitter(alpha = 0.3, color = "tomato") +
geom_boxplot(alpha = 0)
ggplot(data = surveys_complete, mapping = aes(x = species_id, y = weight)) +
geom_violin(alpha = 0) +
geom_jitter(alpha = 0.3, color = "tomato") +
scale_y_log10()
plot_id <- factor()
ggplot(data = surveys_complete, mapping = aes(x = species_id, y = hindfoot_length)) +
geom_violin(alpha = 0) +
geom_jitter(alpha = 0.3) +
geom_point(aes(color = plot_id))
scale_y_log10()
## <ScaleContinuousPosition>
## Range:
## Limits: 0 -- 1
yearly_counts <- surveys_complete %>%
count(year, genus)
ggplot(data = yearly_counts, aes(x = year, y = n, color = genus)) +
geom_line()
yearly_counts %>%
ggplot(mapping = aes(x = year, y = n, color = genus)) +
geom_line()
yearly_counts_graph <- surveys_complete %>%
count(year, genus) %>%
ggplot(mapping = aes(x = year, y = n, color = genus)) +
geom_line()
yearly_counts_graph
### Faceting
ggplot(data = yearly_counts, aes(x = year, y = n)) +
geom_line() +
facet_wrap(facets = vars(genus))
yearly_sex_counts <- surveys_complete %>%
count(year, genus, sex)
ggplot(data = yearly_sex_counts, mapping = aes(x = year, y = n, color = sex)) +
geom_line() +
facet_wrap(facets = vars(genus))
# Facet by Sex and Genus
ggplot(data = yearly_sex_counts,
mapping = aes(x = year, y = n, color = sex)) +
geom_line() +
facet_grid(rows = vars(sex), cols = vars(genus))
# Facet By Rows
ggplot(data = yearly_sex_counts,
mapping = aes(x = year, y = n, color = sex)) +
geom_line() +
facet_grid(rows = vars(genus))
# Facet By Columns
ggplot(data = yearly_sex_counts,
mapping = aes(x = year, y = n, color = sex)) +
geom_line() +
facet_grid(cols = vars(genus))
ggplot(data = yearly_sex_counts,
mapping = aes(x = year, y = n, color = sex)) +
geom_line() +
facet_wrap(vars(genus)) +
theme_bw()
yearly_weight <- surveys_complete %>%
group_by(year, species_id) %>%
summarize(avg_weight = mean(weight))
## `summarise()` regrouping output by 'year' (override with `.groups` argument)
ggplot(data = yearly_weight, mapping = aes(x=year, y=avg_weight)) +
geom_line() +
facet_wrap(vars(species_id)) +
theme_bw()
ggplot(data = yearly_sex_counts, aes(x = year, y = n, color = sex)) +
geom_line() +
facet_wrap(vars(genus)) +
labs(title = "Observed Genera Through Time",
x = "Year of Observation",
y = "Number of Individuals") +
theme_bw()
theme(axis.text.x = element_text(colour = "grey20", size = 12, angle = 90, hjust = 0.5, vjust = 0.5),
axis.text.y = element_text(colour = "grey20", size = 12),
strip.text = element_text(face = "italic"),
text = element_text(size = 16))
## List of 4
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## ..- attr(*, "class")= chr [1:2] "element_text" "element"
## - attr(*, "class")= chr [1:2] "theme" "gg"
## - attr(*, "complete")= logi FALSE
## - attr(*, "validate")= logi TRUE
grey_theme <- theme(axis.text.x = element_text(colour="grey20", size = 12,
angle = 90, hjust = 0.5,
vjust = 0.5),
axis.text.y = element_text(colour = "grey20", size = 12),
text=element_text(size = 16))
ggplot(surveys_complete, aes(x = species_id, y = hindfoot_length)) +
geom_boxplot() +
grey_theme
ggplot(surveys_complete, aes(x = species_id, y = hindfoot_length, color = sex)) +
geom_boxplot() +
theme(axis.text.x = element_text(colour = "purple", size = 12, angle = 90, hjust = 0.5, vjust = 0.5),
axis.text.y = element_text(colour = "blue", size = 12),
strip.text = element_text(face = "italic"),
text = element_text(size = 16))
### Arranging and Exporting Plots
library(gridExtra)
spp_weight_boxplot <- ggplot(data = surveys_complete,
aes(x = species_id, y = weight)) +
geom_boxplot() +
labs(x = "Species",
y = expression(log[10](Weight))) +
scale_y_log10() +
labs()
spp_count_plot <- ggplot(data = yearly_counts,
aes(x = year, y = n, color = genus)) +
geom_line() +
labs(x = "Year", y = "Abundance")
grid.arrange(spp_weight_boxplot, spp_count_plot, ncol = 2, widths = c(4, 6))